Overview

Dataset statistics

Number of variables15
Number of observations18628
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 MiB
Average record size in memory99.0 B

Variable types

DateTime1
Categorical5
Numeric9

Alerts

N_DiaMes is highly overall correlated with Dia_SINHigh correlation
N_Mes is highly overall correlated with Mes_SINHigh correlation
DiaSem_SIN is highly overall correlated with DiaSem and 1 other fieldsHigh correlation
DiaSem_COS is highly overall correlated with DiaSem and 1 other fieldsHigh correlation
Dia_SIN is highly overall correlated with N_DiaMesHigh correlation
Mes_SIN is highly overall correlated with N_MesHigh correlation
DiaSem is highly overall correlated with DiaSem_SIN and 2 other fieldsHigh correlation
Clasif_Lab is highly overall correlated with DiaSem_SIN and 2 other fieldsHigh correlation
Clasif_Fest is highly imbalanced (80.6%)Imbalance
Lab_PrevioFest is highly imbalanced (86.3%)Imbalance
Lab_PostFest is highly imbalanced (85.2%)Imbalance
DiaSem is uniformly distributedUniform
Fecha has unique valuesUnique
DiaSem_SIN has 2661 (14.3%) zerosZeros
Dia_SIN has 612 (3.3%) zerosZeros
Mes_SIN has 1581 (8.5%) zerosZeros

Reproduction

Analysis started2023-10-21 13:53:14.999671
Analysis finished2023-10-21 13:53:30.853030
Duration15.85 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Fecha
Date

Distinct18628
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size145.7 KiB
Minimum2000-01-01 00:00:00
Maximum2050-12-31 00:00:00
2023-10-21T15:53:30.970449image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:31.127189image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

DiaSem
Categorical

HIGH CORRELATION  UNIFORM 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.7 KiB
Sabado
2662 
Domingo
2661 
Jueves
2661 
Lunes
2661 
Martes
2661 
Other values (2)
5322 

Length

Max length9
Median length7
Mean length6.5713979
Min length5

Characters and Unicode

Total characters122412
Distinct characters22
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSabado
2nd rowDomingo
3rd rowLunes
4th rowMartes
5th rowMiercoles

Common Values

ValueCountFrequency (%)
Sabado 2662
14.3%
Domingo 2661
14.3%
Jueves 2661
14.3%
Lunes 2661
14.3%
Martes 2661
14.3%
Miercoles 2661
14.3%
Viernes 2661
14.3%

Length

2023-10-21T15:53:31.283402image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-21T15:53:31.439617image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
sabado 2662
14.3%
domingo 2661
14.3%
jueves 2661
14.3%
lunes 2661
14.3%
martes 2661
14.3%
miercoles 2661
14.3%
viernes 2661
14.3%

Most occurring characters

ValueCountFrequency (%)
e 21288
17.4%
s 13305
10.9%
o 10645
 
8.7%
a 7985
 
6.5%
r 7983
 
6.5%
i 7983
 
6.5%
n 7983
 
6.5%
u 5322
 
4.3%
M 5322
 
4.3%
S 2662
 
2.2%
Other values (12) 31934
26.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 103784
84.8%
Uppercase Letter 18628
 
15.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 21288
20.5%
s 13305
12.8%
o 10645
10.3%
a 7985
 
7.7%
r 7983
 
7.7%
i 7983
 
7.7%
n 7983
 
7.7%
u 5322
 
5.1%
d 2662
 
2.6%
b 2662
 
2.6%
Other values (6) 15966
15.4%
Uppercase Letter
ValueCountFrequency (%)
M 5322
28.6%
S 2662
14.3%
J 2661
14.3%
D 2661
14.3%
L 2661
14.3%
V 2661
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 122412
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 21288
17.4%
s 13305
10.9%
o 10645
 
8.7%
a 7985
 
6.5%
r 7983
 
6.5%
i 7983
 
6.5%
n 7983
 
6.5%
u 5322
 
4.3%
M 5322
 
4.3%
S 2662
 
2.2%
Other values (12) 31934
26.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 122412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 21288
17.4%
s 13305
10.9%
o 10645
 
8.7%
a 7985
 
6.5%
r 7983
 
6.5%
i 7983
 
6.5%
n 7983
 
6.5%
u 5322
 
4.3%
M 5322
 
4.3%
S 2662
 
2.2%
Other values (12) 31934
26.1%

N_DiaMes
Real number (ℝ)

Distinct31
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.729815
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size145.7 KiB
2023-10-21T15:53:31.595829image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8004035
Coefficient of variation (CV)0.55947278
Kurtosis-1.1940459
Mean15.729815
Median Absolute Deviation (MAD)8
Skewness0.0067451021
Sum293015
Variance77.447102
MonotonicityNot monotonic
2023-10-21T15:53:31.705177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 612
 
3.3%
2 612
 
3.3%
28 612
 
3.3%
27 612
 
3.3%
26 612
 
3.3%
25 612
 
3.3%
24 612
 
3.3%
23 612
 
3.3%
22 612
 
3.3%
21 612
 
3.3%
Other values (21) 12508
67.1%
ValueCountFrequency (%)
1 612
3.3%
2 612
3.3%
3 612
3.3%
4 612
3.3%
5 612
3.3%
6 612
3.3%
7 612
3.3%
8 612
3.3%
9 612
3.3%
10 612
3.3%
ValueCountFrequency (%)
31 357
1.9%
30 561
3.0%
29 574
3.1%
28 612
3.3%
27 612
3.3%
26 612
3.3%
25 612
3.3%
24 612
3.3%
23 612
3.3%
22 612
3.3%

N_Mes
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5228688
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size145.7 KiB
2023-10-21T15:53:31.830150image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4488124
Coefficient of variation (CV)0.52872632
Kurtosis-1.2080784
Mean6.5228688
Median Absolute Deviation (MAD)3
Skewness-0.0092717818
Sum121508
Variance11.894307
MonotonicityNot monotonic
2023-10-21T15:53:31.923878image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 1581
8.5%
3 1581
8.5%
5 1581
8.5%
7 1581
8.5%
8 1581
8.5%
10 1581
8.5%
12 1581
8.5%
4 1530
8.2%
6 1530
8.2%
9 1530
8.2%
Other values (2) 2971
15.9%
ValueCountFrequency (%)
1 1581
8.5%
2 1441
7.7%
3 1581
8.5%
4 1530
8.2%
5 1581
8.5%
6 1530
8.2%
7 1581
8.5%
8 1581
8.5%
9 1530
8.2%
10 1581
8.5%
ValueCountFrequency (%)
12 1581
8.5%
11 1530
8.2%
10 1581
8.5%
9 1530
8.2%
8 1581
8.5%
7 1581
8.5%
6 1530
8.2%
5 1581
8.5%
4 1530
8.2%
3 1581
8.5%

N_Ano
Real number (ℝ)

Distinct51
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2024.9993
Minimum2000
Maximum2050
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size145.7 KiB
2023-10-21T15:53:32.048848image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile2002
Q12012
median2025
Q32038
95-th percentile2048
Maximum2050
Range50
Interquartile range (IQR)26

Descriptive statistics

Standard deviation14.720194
Coefficient of variation (CV)0.0072692342
Kurtosis-1.2009234
Mean2024.9993
Median Absolute Deviation (MAD)13
Skewness-5.0292677 × 10-6
Sum37721687
Variance216.68411
MonotonicityIncreasing
2023-10-21T15:53:32.205061image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2000 366
 
2.0%
2044 366
 
2.0%
2032 366
 
2.0%
2024 366
 
2.0%
2020 366
 
2.0%
2036 366
 
2.0%
2016 366
 
2.0%
2012 366
 
2.0%
2040 366
 
2.0%
2008 366
 
2.0%
Other values (41) 14968
80.4%
ValueCountFrequency (%)
2000 366
2.0%
2001 365
2.0%
2002 365
2.0%
2003 365
2.0%
2004 366
2.0%
2005 365
2.0%
2006 365
2.0%
2007 365
2.0%
2008 366
2.0%
2009 365
2.0%
ValueCountFrequency (%)
2050 365
2.0%
2049 365
2.0%
2048 366
2.0%
2047 365
2.0%
2046 365
2.0%
2045 365
2.0%
2044 366
2.0%
2043 365
2.0%
2042 365
2.0%
2041 365
2.0%

Clasif_Lab
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
Laborable
13305 
No Laborable
5323 

Length

Max length12
Median length9
Mean length9.8572579
Min length9

Characters and Unicode

Total characters183621
Distinct characters9
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo Laborable
2nd rowNo Laborable
3rd rowLaborable
4th rowLaborable
5th rowLaborable

Common Values

ValueCountFrequency (%)
Laborable 13305
71.4%
No Laborable 5323
28.6%

Length

2023-10-21T15:53:32.486246image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-21T15:53:32.611217image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
laborable 18628
77.8%
no 5323
 
22.2%

Most occurring characters

ValueCountFrequency (%)
a 37256
20.3%
b 37256
20.3%
o 23951
13.0%
L 18628
10.1%
r 18628
10.1%
l 18628
10.1%
e 18628
10.1%
N 5323
 
2.9%
5323
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 154347
84.1%
Uppercase Letter 23951
 
13.0%
Space Separator 5323
 
2.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 37256
24.1%
b 37256
24.1%
o 23951
15.5%
r 18628
12.1%
l 18628
12.1%
e 18628
12.1%
Uppercase Letter
ValueCountFrequency (%)
L 18628
77.8%
N 5323
 
22.2%
Space Separator
ValueCountFrequency (%)
5323
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 178298
97.1%
Common 5323
 
2.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 37256
20.9%
b 37256
20.9%
o 23951
13.4%
L 18628
10.4%
r 18628
10.4%
l 18628
10.4%
e 18628
10.4%
N 5323
 
3.0%
Common
ValueCountFrequency (%)
5323
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 183621
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 37256
20.3%
b 37256
20.3%
o 23951
13.0%
L 18628
10.1%
r 18628
10.1%
l 18628
10.1%
e 18628
10.1%
N 5323
 
2.9%
5323
 
2.9%

Clasif_Fest
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
No Festivo
18072 
Festivo
 
556

Length

Max length10
Median length10
Mean length9.9104574
Min length7

Characters and Unicode

Total characters184612
Distinct characters9
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFestivo
2nd rowNo Festivo
3rd rowNo Festivo
4th rowNo Festivo
5th rowNo Festivo

Common Values

ValueCountFrequency (%)
No Festivo 18072
97.0%
Festivo 556
 
3.0%

Length

2023-10-21T15:53:32.736217image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-21T15:53:32.861158image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
festivo 18628
50.8%
no 18072
49.2%

Most occurring characters

ValueCountFrequency (%)
o 36700
19.9%
F 18628
10.1%
e 18628
10.1%
s 18628
10.1%
t 18628
10.1%
i 18628
10.1%
v 18628
10.1%
N 18072
9.8%
18072
9.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 129840
70.3%
Uppercase Letter 36700
 
19.9%
Space Separator 18072
 
9.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 36700
28.3%
e 18628
14.3%
s 18628
14.3%
t 18628
14.3%
i 18628
14.3%
v 18628
14.3%
Uppercase Letter
ValueCountFrequency (%)
F 18628
50.8%
N 18072
49.2%
Space Separator
ValueCountFrequency (%)
18072
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 166540
90.2%
Common 18072
 
9.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 36700
22.0%
F 18628
11.2%
e 18628
11.2%
s 18628
11.2%
t 18628
11.2%
i 18628
11.2%
v 18628
11.2%
N 18072
10.9%
Common
ValueCountFrequency (%)
18072
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 184612
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 36700
19.9%
F 18628
10.1%
e 18628
10.1%
s 18628
10.1%
t 18628
10.1%
i 18628
10.1%
v 18628
10.1%
N 18072
9.8%
18072
9.8%

Lab_PrevioFest
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size145.7 KiB
0
18269 
1
 
359

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters18628
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 18269
98.1%
1 359
 
1.9%

Length

2023-10-21T15:53:32.970508image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-21T15:53:33.111076image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0 18269
98.1%
1 359
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 18269
98.1%
1 359
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 18628
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 18269
98.1%
1 359
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Common 18628
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 18269
98.1%
1 359
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18628
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18269
98.1%
1 359
 
1.9%

Lab_PostFest
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size145.7 KiB
0
18232 
1
 
396

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters18628
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 18232
97.9%
1 396
 
2.1%

Length

2023-10-21T15:53:33.220426image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-21T15:53:33.345419image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0 18232
97.9%
1 396
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 18232
97.9%
1 396
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 18628
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 18232
97.9%
1 396
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 18628
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 18232
97.9%
1 396
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18628
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18232
97.9%
1 396
 
2.1%

DiaSem_SIN
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-5.2336693 × 10-5
Minimum-0.97492791
Maximum0.97492791
Zeros2661
Zeros (%)14.3%
Negative7984
Negative (%)42.9%
Memory size145.7 KiB
2023-10-21T15:53:33.439141image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-0.97492791
5-th percentile-0.97492791
Q1-0.78183148
median0
Q30.78183148
95-th percentile0.97492791
Maximum0.97492791
Range1.9498558
Interquartile range (IQR)1.563663

Descriptive statistics

Standard deviation0.70714286
Coefficient of variation (CV)-13511.417
Kurtosis-1.5001122
Mean-5.2336693 × 10-5
Median Absolute Deviation (MAD)0.78183148
Skewness8.1356464 × 10-5
Sum-0.97492791
Variance0.50005102
MonotonicityNot monotonic
2023-10-21T15:53:33.532876image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
-0.9749279122 2662
14.3%
-0.7818314825 2661
14.3%
0 2661
14.3%
0.7818314825 2661
14.3%
0.9749279122 2661
14.3%
0.4338837391 2661
14.3%
-0.4338837391 2661
14.3%
ValueCountFrequency (%)
-0.9749279122 2662
14.3%
-0.7818314825 2661
14.3%
-0.4338837391 2661
14.3%
0 2661
14.3%
0.4338837391 2661
14.3%
0.7818314825 2661
14.3%
0.9749279122 2661
14.3%
ValueCountFrequency (%)
0.9749279122 2661
14.3%
0.7818314825 2661
14.3%
0.4338837391 2661
14.3%
0 2661
14.3%
-0.4338837391 2661
14.3%
-0.7818314825 2661
14.3%
-0.9749279122 2662
14.3%

DiaSem_COS
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.1945509 × 10-5
Minimum-0.90096887
Maximum1
Zeros0
Zeros (%)0.0%
Negative10645
Negative (%)57.1%
Memory size145.7 KiB
2023-10-21T15:53:33.657847image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-0.90096887
5-th percentile-0.90096887
Q1-0.90096887
median-0.22252093
Q30.6234898
95-th percentile1
Maximum1
Range1.9009689
Interquartile range (IQR)1.5244587

Descriptive statistics

Standard deviation0.70710866
Coefficient of variation (CV)-59194.521
Kurtosis-1.5000154
Mean-1.1945509 × 10-5
Median Absolute Deviation (MAD)0.67844793
Skewness4.9012569 × 10-5
Sum-0.22252093
Variance0.50000266
MonotonicityNot monotonic
2023-10-21T15:53:33.751575image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
-0.222520934 2662
14.3%
0.6234898019 2661
14.3%
1 2661
14.3%
0.6234898019 2661
14.3%
-0.222520934 2661
14.3%
-0.9009688679 2661
14.3%
-0.9009688679 2661
14.3%
ValueCountFrequency (%)
-0.9009688679 2661
14.3%
-0.9009688679 2661
14.3%
-0.222520934 2662
14.3%
-0.222520934 2661
14.3%
0.6234898019 2661
14.3%
0.6234898019 2661
14.3%
1 2661
14.3%
ValueCountFrequency (%)
1 2661
14.3%
0.6234898019 2661
14.3%
0.6234898019 2661
14.3%
-0.222520934 2661
14.3%
-0.222520934 2662
14.3%
-0.9009688679 2661
14.3%
-0.9009688679 2661
14.3%

Dia_SIN
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct112
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.1030083 × 10-18
Minimum-1
Maximum1
Zeros612
Zeros (%)3.3%
Negative8887
Negative (%)47.7%
Memory size145.7 KiB
2023-10-21T15:53:33.876546image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-0.98846832
Q1-0.72479279
median0
Q30.72479279
95-th percentile0.98846832
Maximum1
Range2
Interquartile range (IQR)1.4495856

Descriptive statistics

Standard deviation0.70706882
Coefficient of variation (CV)1.1585579 × 1017
Kurtosis-1.4998389
Mean6.1030083 × 10-18
Median Absolute Deviation (MAD)0.72479279
Skewness-3.257887 × 10-17
Sum5.6843419 × 10-14
Variance0.49994631
MonotonicityNot monotonic
2023-10-21T15:53:34.017138image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 612
 
3.3%
-0.101168322 357
 
1.9%
0.2012985201 357
 
1.9%
-0.2012985201 357
 
1.9%
-0.5712682151 357
 
1.9%
-0.7247927872 357
 
1.9%
-0.8486442575 357
 
1.9%
-0.9377521321 357
 
1.9%
-0.9884683243 357
 
1.9%
-0.9987165072 357
 
1.9%
Other values (102) 14803
79.5%
ValueCountFrequency (%)
-1 41
 
0.2%
-0.9987165072 357
1.9%
-0.9985334139 10
 
0.1%
-0.9945218954 204
1.1%
-0.9945218954 204
1.1%
-0.9884683243 357
1.9%
-0.9868265225 10
 
0.1%
-0.9749279122 82
 
0.4%
-0.9680771189 357
1.9%
-0.9635499925 10
 
0.1%
ValueCountFrequency (%)
1 41
 
0.2%
0.9987165072 357
1.9%
0.9985334139 10
 
0.1%
0.9945218954 204
1.1%
0.9945218954 204
1.1%
0.9884683243 357
1.9%
0.9868265225 10
 
0.1%
0.9749279122 82
 
0.4%
0.9680771189 357
1.9%
0.9635499925 10
 
0.1%

Dia_COS
Real number (ℝ)

Distinct103
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00016104788
Minimum-1
Maximum1
Zeros0
Zeros (%)0.0%
Negative9486
Negative (%)50.9%
Memory size145.7 KiB
2023-10-21T15:53:34.173352image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-0.99486932
Q1-0.66913061
median-0.050649169
Q30.68896692
95-th percentile0.97952994
Maximum1
Range2
Interquartile range (IQR)1.3580975

Descriptive statistics

Standard deviation0.70718268
Coefficient of variation (CV)4391.133
Kurtosis-1.500161
Mean0.00016104788
Median Absolute Deviation (MAD)0.71977978
Skewness-0.00022782945
Sum3
Variance0.50010735
MonotonicityNot monotonic
2023-10-21T15:53:34.329565image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9795299413 714
 
3.8%
0.3473052528 714
 
3.8%
0.9189578116 714
 
3.8%
0.8207634412 714
 
3.8%
-0.9948693234 714
 
3.8%
0.5289640103 714
 
3.8%
1 615
 
3.3%
0.1514277775 357
 
1.9%
-0.8743466161 357
 
1.9%
-0.05064916884 357
 
1.9%
Other values (93) 12658
68.0%
ValueCountFrequency (%)
-1 245
 
1.3%
-0.9948693234 714
3.8%
-0.9941379572 20
 
0.1%
-0.9781476007 204
 
1.1%
-0.9781476007 204
 
1.1%
-0.9749279122 82
 
0.4%
-0.9541392564 357
1.9%
-0.9541392564 357
1.9%
-0.9476531712 20
 
0.1%
-0.9135454576 204
 
1.1%
ValueCountFrequency (%)
1 615
3.3%
0.9795299413 714
3.8%
0.9781476007 204
 
1.1%
0.9781476007 204
 
1.1%
0.9766205557 20
 
0.1%
0.9749279122 41
 
0.2%
0.9749279122 41
 
0.2%
0.9189578116 714
3.8%
0.9135454576 204
 
1.1%
0.9135454576 204
 
1.1%

Mes_SIN
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.003122472
Minimum-1
Maximum1
Zeros1581
Zeros (%)8.5%
Negative7803
Negative (%)41.9%
Memory size145.7 KiB
2023-10-21T15:53:34.454536image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-0.5
median0
Q30.8660254
95-th percentile1
Maximum1
Range2
Interquartile range (IQR)1.3660254

Descriptive statistics

Standard deviation0.70699548
Coefficient of variation (CV)-226.42172
Kurtosis-1.497013
Mean-0.003122472
Median Absolute Deviation (MAD)0.8660254
Skewness0.011940837
Sum-58.165409
Variance0.49984261
MonotonicityNot monotonic
2023-10-21T15:53:34.563885image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 1581
8.5%
0.8660254038 1581
8.5%
0.8660254038 1581
8.5%
1.224646799 × 10-161581
8.5%
-0.5 1581
8.5%
-1 1581
8.5%
-0.5 1581
8.5%
1 1530
8.2%
0.5 1530
8.2%
-0.8660254038 1530
8.2%
Other values (2) 2971
15.9%
ValueCountFrequency (%)
-1 1581
8.5%
-0.8660254038 1530
8.2%
-0.8660254038 1530
8.2%
-0.5 1581
8.5%
-0.5 1581
8.5%
0 1581
8.5%
1.224646799 × 10-161581
8.5%
0.5 1441
7.7%
0.5 1530
8.2%
0.8660254038 1581
8.5%
ValueCountFrequency (%)
1 1530
8.2%
0.8660254038 1581
8.5%
0.8660254038 1581
8.5%
0.5 1530
8.2%
0.5 1441
7.7%
1.224646799 × 10-161581
8.5%
0 1581
8.5%
-0.5 1581
8.5%
-0.5 1581
8.5%
-0.8660254038 1530
8.2%

Mes_COS
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.0041376563
Minimum-1
Maximum1
Zeros0
Zeros (%)0.0%
Negative9384
Negative (%)50.4%
Memory size145.7 KiB
2023-10-21T15:53:34.673236image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-0.8660254
median-1.8369702 × 10-16
Q30.5
95-th percentile1
Maximum1
Range2
Interquartile range (IQR)1.3660254

Descriptive statistics

Standard deviation0.70723702
Coefficient of variation (CV)-170.92696
Kurtosis-1.4977643
Mean-0.0041376563
Median Absolute Deviation (MAD)0.5
Skewness0.0087795818
Sum-77.076261
Variance0.5001842
MonotonicityNot monotonic
2023-10-21T15:53:34.782585image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 1581
8.5%
0.5 1581
8.5%
-0.5 1581
8.5%
-1 1581
8.5%
-0.8660254038 1581
8.5%
-1.836970199 × 10-161581
8.5%
0.8660254038 1581
8.5%
6.123233996 × 10-171530
8.2%
-0.8660254038 1530
8.2%
-0.5 1530
8.2%
Other values (2) 2971
15.9%
ValueCountFrequency (%)
-1 1581
8.5%
-0.8660254038 1581
8.5%
-0.8660254038 1530
8.2%
-0.5 1530
8.2%
-0.5 1581
8.5%
-1.836970199 × 10-161581
8.5%
6.123233996 × 10-171530
8.2%
0.5 1530
8.2%
0.5 1581
8.5%
0.8660254038 1581
8.5%
ValueCountFrequency (%)
1 1581
8.5%
0.8660254038 1441
7.7%
0.8660254038 1581
8.5%
0.5 1581
8.5%
0.5 1530
8.2%
6.123233996 × 10-171530
8.2%
-1.836970199 × 10-161581
8.5%
-0.5 1581
8.5%
-0.5 1530
8.2%
-0.8660254038 1530
8.2%

Interactions

2023-10-21T15:53:28.785645image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:15.975815image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:17.501366image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:19.037721image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:20.502100image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:22.176141image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:23.750350image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:25.702598image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:27.250715image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:28.950946image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:16.122817image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:17.655604image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:19.205141image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:20.668397image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:22.335999image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:23.926574image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:25.866800image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:27.433487image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:29.121879image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:16.307816image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:17.801601image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:19.349559image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:20.852287image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:22.484283image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:24.083728image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:26.035543image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:27.590076image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:29.282473image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:16.470026image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:17.959433image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:19.501288image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:21.032266image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:22.650764image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:24.252015image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:26.206955image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:27.733986image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:29.457502image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:16.680548image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:18.127781image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:19.668317image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:21.257253image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:22.833540image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:24.433717image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:26.383429image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:27.921369image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:29.619656image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:16.842090image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:18.311549image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:19.841013image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:21.439929image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:23.009676image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:24.619942image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:26.557961image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:28.092288image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:29.807886image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:17.005075image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:18.494088image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:20.011487image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:21.638956image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:23.202868image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:24.835705image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:26.729828image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:28.266959image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:29.987828image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:17.178031image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:18.673104image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:20.176051image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:21.826499image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:23.384155image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:25.016738image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:26.908345image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:28.444647image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:30.167615image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:17.341298image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:18.868886image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:20.352645image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:22.005549image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:23.575850image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:25.233143image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:27.068638image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-10-21T15:53:28.621577image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2023-10-21T15:53:34.907554image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
N_DiaMesN_MesN_AnoDiaSem_SINDiaSem_COSDia_SINDia_COSMes_SINMes_COSDiaSemClasif_LabClasif_FestLab_PrevioFestLab_PostFest
N_DiaMes1.0000.010-0.0000.0000.000-0.742-0.106-0.005-0.0100.0000.0000.1470.1740.127
N_Mes0.0101.0000.000-0.000-0.0000.000-0.002-0.717-0.3250.0000.0000.1490.1380.140
N_Ano-0.0000.0001.0000.000-0.0000.000-0.000-0.000-0.0000.0000.0000.0000.0000.000
DiaSem_SIN0.000-0.0000.0001.0000.214-0.000-0.0000.000-0.0001.0001.0000.0480.0920.099
DiaSem_COS0.000-0.000-0.0000.2141.000-0.000-0.000-0.000-0.0001.0000.5480.0460.0570.035
Dia_SIN-0.7420.0000.000-0.000-0.0001.0000.0050.000-0.0000.0000.0000.2060.1450.159
Dia_COS-0.106-0.002-0.000-0.000-0.0000.0051.000-0.0010.0020.0000.0000.1540.1170.147
Mes_SIN-0.005-0.717-0.0000.000-0.0000.000-0.0011.000-0.0040.0000.0000.1590.1350.146
Mes_COS-0.010-0.325-0.000-0.000-0.000-0.0000.002-0.0041.0000.0000.0000.1180.1320.112
DiaSem0.0000.0000.0001.0001.0000.0000.0000.0000.0001.0001.0000.0470.0910.107
Clasif_Lab0.0000.0000.0001.0000.5480.0000.0000.0000.0001.0001.0000.0180.0880.093
Clasif_Fest0.1470.1490.0000.0480.0460.2060.1540.1590.1180.0470.0181.0000.0010.133
Lab_PrevioFest0.1740.1380.0000.0920.0570.1450.1170.1350.1320.0910.0880.0011.0000.078
Lab_PostFest0.1270.1400.0000.0990.0350.1590.1470.1460.1120.1070.0930.1330.0781.000

Missing values

2023-10-21T15:53:30.416767image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-21T15:53:30.717407image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

FechaDiaSemN_DiaMesN_MesN_AnoClasif_LabClasif_FestLab_PrevioFestLab_PostFestDiaSem_SINDiaSem_COSDia_SINDia_COSMes_SINMes_COS
02000-01-01Sabado112000No LaborableFestivo00-0.97-0.220.001.000.001.00
12000-01-02Domingo212000No LaborableNo Festivo00-0.780.620.200.980.001.00
22000-01-03Lunes312000LaborableNo Festivo000.001.000.390.920.001.00
32000-01-04Martes412000LaborableNo Festivo000.780.620.570.820.001.00
42000-01-05Miercoles512000LaborableNo Festivo100.97-0.220.720.690.001.00
52000-01-06Jueves612000LaborableFestivo000.43-0.900.850.530.001.00
62000-01-07Viernes712000LaborableNo Festivo01-0.43-0.900.940.350.001.00
72000-01-08Sabado812000No LaborableNo Festivo00-0.97-0.220.990.150.001.00
82000-01-09Domingo912000No LaborableNo Festivo00-0.780.621.00-0.050.001.00
92000-01-10Lunes1012000LaborableNo Festivo000.001.000.97-0.250.001.00
FechaDiaSemN_DiaMesN_MesN_AnoClasif_LabClasif_FestLab_PrevioFestLab_PostFestDiaSem_SINDiaSem_COSDia_SINDia_COSMes_SINMes_COS
186182050-12-22Jueves22122050LaborableNo Festivo000.43-0.90-0.90-0.44-0.500.87
186192050-12-23Viernes23122050LaborableNo Festivo00-0.43-0.90-0.97-0.25-0.500.87
186202050-12-24Sabado24122050No LaborableNo Festivo00-0.97-0.22-1.00-0.05-0.500.87
186212050-12-25Domingo25122050No LaborableFestivo00-0.780.62-0.990.15-0.500.87
186222050-12-26Lunes26122050LaborableFestivo010.001.00-0.940.35-0.500.87
186232050-12-27Martes27122050LaborableNo Festivo010.780.62-0.850.53-0.500.87
186242050-12-28Miercoles28122050LaborableNo Festivo000.97-0.22-0.720.69-0.500.87
186252050-12-29Jueves29122050LaborableNo Festivo000.43-0.90-0.570.82-0.500.87
186262050-12-30Viernes30122050LaborableNo Festivo00-0.43-0.90-0.390.92-0.500.87
186272050-12-31Sabado31122050No LaborableNo Festivo00-0.97-0.22-0.200.98-0.500.87